Instructions to use rqadri/deepmath4b_100s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rqadri/deepmath4b_100s with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "rqadri/deepmath4b_100s")

Transformers

How to use rqadri/deepmath4b_100s with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rqadri/deepmath4b_100s")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rqadri/deepmath4b_100s", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rqadri/deepmath4b_100s with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rqadri/deepmath4b_100s"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rqadri/deepmath4b_100s",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rqadri/deepmath4b_100s

SGLang

How to use rqadri/deepmath4b_100s with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rqadri/deepmath4b_100s" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rqadri/deepmath4b_100s",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rqadri/deepmath4b_100s" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rqadri/deepmath4b_100s",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use rqadri/deepmath4b_100s with Docker Model Runner:
```
docker model run hf.co/rqadri/deepmath4b_100s
```

deepmath4b_100s / optimizer.pt

rqadri

Upload folder using huggingface_hub

e86739c verified about 1 year ago

History Blame Contribute Delete

135 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: c2a1e6c6a0c1ef11647567f11d72e7ae8c8eda126a26b4c65214a8e5038aa036
Size of remote file:: 135 MB
SHA256:: 299b6e818d0d8b3baac7cad18c8c561fddafdae146610061dfab01bcead0bba9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.